干预规则挖掘是近年从干预实践中提出的新型数据挖掘任务，旨在利用数据挖掘技术探测干预事件，发现最佳干预时机和力度，提供促进事物向期待状态转化的决策支持。文中以四年的研究实践为背景，介绍干预规则挖掘的研究沿革和现状，给出了干预规则挖掘的任务分类。从三个角度，即干预效果预测、干预方法发现和未知干预探测三方面,介绍干预规则挖掘的研究问题，困难和成果。展望了干预规则挖掘未来研究方向。

Gene Expression Programming (GEP) aims at discovering essential rules hidden in observed data and expressing them mathematically. GEP has been proved to be a powerful tool for constructing efficient classifiers. Traditional GEP-classifiers ignore the distribution of samples, and hence decrease the efficiency and accuracy. The contributions of this paper include: (1) proposing two strategies of generating classification threshold dynamically, (2) designing a new approach called Distance Guided Evolution Algorithm (DGEA) to improve the efficiency of GEP, and (3) demonstrating the effectiveness of generating classification threshold dynamically and DGEA by extensive experiments. The results show that the new methods decrease the number of evolutional generations by 83% to 90%, and increase the accuracy by 20% compared with the traditional approach.